Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
This work addresses revenue optimization for advertising platforms like Meituan, but it is incremental as it builds on existing user behavior modeling by incorporating page-level information.
The paper tackles the problem of maximizing revenue in ads allocation by modeling user preferences using page-level feedback and multiple feedback types, demonstrating that their Deep Page-level Interest Network increases platform revenue through offline and online experiments.
A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, modeling user preference with historical behavior is essential in recommendation and advertising (e.g., CTR prediction and ads allocation). Most previous works for user behavior modeling only model user's historical point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback as input, and capture user preference for item arrangement under different receptive fields through the multi-channel interaction module. Through extensive offline and online experiments on Meituan food delivery platform, we demonstrate that DPIN can effectively model the page-level user preference and increase the revenue for the platform.